Institutional Underpinnings and Democracy Backsliding in the Perspective of COVID-19

24 05 2020

Goals of the project

reproduction of initial results
robustness checks
systematization of files and folders?

Problems

updated indexes
mix of R and Python?

Research purpose

COVID-19 and its economic and legal consequences are undoubtedly a very important problem that we have to face.
Governments are using the global pandemic to pursue their opportunistic goals, corruption and power strengthening.
- Therefore, pandemic is concerned as a serious threat to democracy, as governments may try to limit democratic rules under the cover of pandemic management.
- On the other hand, well-managed lockdowns and other means of government interventionism may lead to increased satisfaction with democracy or trust in government.
An alleged democracy recession can be observed in recent years and this backsliding is deeply rooted in institutional environment.
The phenomenon of democracy backsliding remains unexplained despite many attempts.

Research question

What is the relevance of the law and the current state of democracy for susceptibility to democracy backsliding in the face of the COVID-19?

Materials

Group of variables	Variables	Time range
Pandemic data	Pandemic Democratic Violations Index (pandem), Pandemic Democratic Violations Disinformation Index (pandem_dis), Pandemic Backsliding Index (panback)	03.2020 - 07.2020
Democratization data	Rule of law index (rule), Electoral democracy index (polyarchy), Education level index (education), Politico-geographic regions (region_geo)	2019
Economic & Demographic data	Gini Index (gini), GDP pc (gdp_pc), Trade GDP (trade_gdp), Inflation rate (inflation), Oil sale (oil), Minerals sale (mineral),Population Density (density), Income group (income_group)	mostly 2019
Fractionalization data	Ethnic (ethnic_frac), Linguistic (ling_frac) and Religious fractionalization (relig_frac)	2002

Adjustments

cases_ratio
robustness checks
- time periods
- collinearity
- missing values
adding squares term

Initial results - OLS with categorical variables


	OLS models:

	(1) (all variables)			(2) (all variables, excluding polyarchy)			(3) (all variables, excluding rule)
	panback	pandem	pandem_dis	panback	pandem	pandem_dis	panback	pandem	pandem_dis

rule	0.067	0.033	0.027	0.119^**	-0.105^*	-0.097^*	-	-	-

polyarchy	0.091	-0.239^**	-0.216^**	-	-	-	0.145^**	-0.212^***	-0.194^***

education	-0.008	-0.0003	-0.003	-0.006	-0.005	-0.006	-0.008	-0.001	-0.003

ethnic_frac	0.057	-0.0003	0.013	0.046	0.029	0.040	0.066	0.004	0.017

ling_frac	0.017	0.024	-0.006	0.026	0.0003	-0.027	0.015	0.022	-0.007

relig_frac	0.022	-0.015	-0.006	0.019	-0.007	0.002	0.024	-0.013	-0.005

gdp_pc_log	0.010	0.019	0.019	0.004	0.037	0.035	0.017	0.022	0.022

gini_log	0.047	0.075	0.075	0.041	0.090	0.089	0.046	0.074	0.075

inflation	-0.0002	0.00000	-0.0001	-0.0001	-0.0001	-0.0002	-0.0002	-0.00003	-0.0002

trade_gdp_log	-0.045^*	-0.068^**	-0.068^***	-0.043^*	-0.072^**	-0.071^***	-0.044^*	-0.067^**	-0.067^***

oil	0.002	0.001	0.001	0.002	0.001	0.001	0.002	0.001	0.001

mineral	0.004	0.006^**	0.006^**	0.004	0.006^*	0.006^*	0.003	0.006^**	0.006^**

density_log	0.013	0.010	0.010	0.014	0.009	0.009	0.013	0.010	0.010

region_geo FE	yes	yes	yes	yes	yes	yes	yes	yes	yes

income_group FE	yes	yes	yes	yes	yes	yes	yes	yes	yes

Constant	-0.136	0.068	0.047	-0.066	-0.114	-0.118	-0.170	0.051	0.033

Observations	146	146	146	146	146	146	146	146	146
Adjusted R²	0.218	0.318	0.313	0.216	0.283	0.281	0.218	0.323	0.318

Note:	^p<0.1; ^p<0.05; ^**p<0.01

Initial results - OLS without categorical variables


	OLS models:

	(1) (all variables)			(2) (all variables, excluding polyarchy)			(3) (all variables, excluding rule)
	panback	pandem	pandem_dis	panback	pandem	pandem_dis	panback	pandem	pandem_dis

rule	0.019	0.020	-0.008	0.053	-0.148^***	-0.152^***	-	-	-

polyarchy	0.055	-0.272^***	-0.233^***	-	-	-	0.069	-0.257^***	-0.239^***

education	-0.003	0.001	0.0005	-0.003	0.002	0.001	-0.003	0.001	0.001

ethnic_frac	0.029	-0.013	0.009	0.028	-0.008	0.014	0.030	-0.012	0.009

ling_frac	0.034	0.035	0.008	0.032	0.044	0.015	0.035	0.036	0.007

relig_frac	-0.029	-0.044	-0.030	-0.026	-0.060	-0.044	-0.029	-0.044	-0.030

gdp_pc_log	-0.014	-0.002	-0.001	-0.014	-0.005	-0.003	-0.013	-0.0004	-0.001

gini_log	0.056	0.099^*	0.094^*	0.054	0.109^*	0.102^*	0.056	0.099^*	0.094^*

inflation	-0.0003	-0.0001	-0.0003	-0.0003	-0.0003	-0.0004	-0.0003	-0.0001	-0.0003

trade_gdp_log	-0.019	-0.039^*	-0.039^*	-0.019	-0.037	-0.038^*	-0.019	-0.039^*	-0.039^*

oil	0.001	-0.0001	-0.0002	0.0002	0.001	0.001	0.0005	-0.0002	-0.0002

mineral	0.001	0.003	0.003	0.001	0.003	0.002	0.001	0.003	0.003

density_log	0.015^*	0.018^**	0.014^*	0.014^*	0.023^**	0.018^**	0.015^*	0.018^**	0.013^*

region_geo FE	no	no	no	no	no	no	no	no	no

income_group FE	no	no	no	no	no	no	no	no	no

Constant	0.019	0.055	0.065	0.037	-0.035	-0.012	0.008	0.043	0.070


Observations	146	146	146	146	146	146	146	146	146
Adjusted R²	0.057	0.276	0.276	0.059	0.202	0.215	0.063	0.281	0.282

Note:	^p<0.1; ^p<0.05; ^**p<0.01

Repro results - OLS with categorical variables


	OLS models:

	(1) (all variables)		(2) (all variables, excluding polyarchy)		(3) (all variables, excluding rule)
	panback	pandem	panback	pandem	panback	pandem

rule	0.539^***	-0.021	0.734^***	-0.149^**	-	-

rule2	-0.506^***	-	-0.676^***	-	-	-

polyarchy	0.702^***	-0.216^**	-	-	0.977^***	-0.233^***

polyarchy2	-0.696^***	-	-	-	-0.965^***	-

cases_ratio	0.00000	-0.00000	0.00000	-0.00000	-0.00000	-0.00000

education_log	-0.020	-0.026	-0.026	-0.047	-0.034	-0.024

ethnic_frac	-0.051	-0.001	-0.059	0.032	0.009	-0.004

ling_frac	0.027	0.019	0.044	-0.005	0.029	0.021

relig_frac	-0.006	-0.030	0.008	-0.021	0.001	-0.031

gdp_pc_log	0.024	0.015	0.016	0.031	0.028	0.013

gini_log	0.067	0.141	0.060	0.165^*	0.091	0.140

inflation	0.0005^*	0.001^*	0.001^**	0.001	0.0001	0.001^*

trade_gdp_log	0.014	-0.003	0.026	-0.005	0.005	-0.003

oil	-0.001	-0.004^*	-0.001	-0.003	-0.001	-0.004^*

mineral	-0.0003	0.002	-0.0001	0.002	-0.0004	0.002

density_log	0.005	0.008	0.011	0.007	0.003	0.008

region_geo FE	yes	yes	yes	yes	yes	yes

income_group FE	yes	yes	yes	yes	yes	yes

Constant	-0.650^*	-0.366	-0.563	-0.608	-0.693^*	-0.349

Observations	143	143	143	143	143	143
Adjusted R²	0.335	0.275	0.286	0.252	0.281	0.281

Note:	^p<0.1; ^p<0.05; ^**p<0.01

Repro results - OLS without categorical variables


	OLS models:

	(1) (all variables)		(2) (all variables, excluding polyarchy)		(3) (all variables, excluding rule)
	panback	pandem	panback	pandem	panback	pandem

rule	0.475^***	-0.064	0.734^***	-0.199^***	-	-

rule2	-0.459^***	-	-0.708^***	-	-	-

polyarchy	0.721^***	-0.221^***	-	-	1.081^***	-0.268^***

polyarchy2	-0.726^***	-	-	-	-1.121^***	-

cases_ratio	0.00000	0.00000	0.00000	0.00000	-0.00000	0.00000

education_log	-0.009	0.001	-0.016	-0.0003	-0.004	0.006

ethnic_frac	-0.041	0.013	-0.043	0.023	-0.007	0.010

ling_frac	0.051	0.042	0.060	0.046	0.044	0.039

relig_frac	-0.054^*	-0.099^*	-0.035	-0.110^**	-0.073^*	-0.100^*

gdp_pc_log	0.010	0.0005	0.002	0.0005	0.012	-0.005

gini_log	0.079^**	0.113^*	0.086^*	0.116^*	0.082^*	0.113^*

inflation	0.0004^*	0.0005	0.0005	0.0003	0.00003	0.001

trade_gdp_log	-0.004	-0.004	0.007	-0.003	-0.011	-0.005

oil	-0.001^**	-0.003	-0.002	-0.001	-0.001	-0.003

mineral	-0.001	-0.001	-0.002	-0.001	-0.001	-0.001

density_log	0.010	0.007	0.011	0.010	0.010	0.006

region_geo FE	no	no	no	no	no	no

income_group FE	no	no	no	no	no	no

Constant	-0.432^***	-0.016	-0.355^*	-0.084	-0.422^**	0.019

Observations	143	143	143	143	143	143
Adjusted R²	0.336	0.252	0.278	0.211	0.285	0.253

Note:	^p<0.1; ^p<0.05; ^**p<0.01

Robustness check - collinearity


	OLS models:

	(1)		(2)		(3)
	panback	pandem	panback	pandem	panback	pandem

rule	0.538^***	-0.042	0.769^***	-0.175^***	-	-

rule2	-0.510^***	-	-0.724^***	-	-	-

polyarchy	0.728^***	-0.220^**	-	-	1.050^***	-0.256^***

polyarchy2	-0.723^***	-	-	-	-1.059^***	-




Control variables:	cases_ratio, education_log, fractionalization, gdp_pc_log, inflation, trade_gdp_log, oil, mineral, density_log, region_geo


Note:	^p<0.1; ^p<0.05; ^**p<0.01

Robustness check - collinearity


	OLS models:

	(1)		(2)		(3)
	panback	pandem	panback	pandem	panback	pandem

rule	0.491^***	-0.060	0.744^***	-0.206^***	-	-

rule2	-0.474^***	-	-0.719^***	-	-	-

polyarchy	0.726^***	-0.237^***	-	-	1.108^***	-0.281^***

polyarchy2	-0.737^***	-	-	-	-1.158^***	-




Control variables:	cases_ratio, education_log, fractionalization, gdp_pc_log, inflation, trade_gdp_log, oil, mineral, density_log


Note:	^p<0.1; ^p<0.05; ^**p<0.01

Robustness check - missings


	OLS models:

	(1)		(2)		(3)
	panback	pandem	panback	pandem	panback	pandem

rule	0.575^***	-0.006	0.773^***	-0.158^**	-	-

rule2	-0.535^***	-	-0.719^***	-	-	-

polyarchy	0.702^***	-0.254^***	-	-	1.011^***	-0.259^***

polyarchy2	-0.717^***	-	-	-	-1.025^***	-




Control variables:	cases_ratio, ethnic_frac, ling_frac, relig_frac, gdp_pc_log, inflation, trade_gdp_log, oil, mineral, density_log, region_geo, income_categories


Note:	^p<0.1; ^p<0.05; ^**p<0.01

Robustness check - missings


	OLS models:

	(1)		(2)		(3)
	panback	pandem	panback	pandem	panback	pandem

rule	0.478^***	-0.061	0.751^***	-0.198^***	-	-

rule2	-0.457^***	-	-0.719^***	-	-	-

polyarchy	0.752^***	-0.224^***	-	-	1.110^***	-0.270^***

polyarchy2	-0.762^***	-	-	-	-1.151^***	-




Control variables:	cases_ratio, ethnic_frac, ling_frac, relig_frac, gdp_pc_log, inflation, trade_gdp_log, oil, mineral, density_log


Note:	^p<0.1; ^p<0.05; ^**p<0.01

Initial results - Random Forest (SHAP dependence plots)

Repro results - Random Forest (SHAP dependence plots)

Initial results - Catboost (SHAP summary plots)

Repro results - Catboost (SHAP summary plots)

Initial results - Random Forest (2D Partial Dependence Plots)

Repro results - Random Forest (2D Partial Dependence Plots)

Conclusions

Influence of law and state of democracy is exceptionally strong. The stronger the rule of law and the higher the level of democracy, the lower is the risk of democracy backsliding in the face of global pandemic.
Our study provides other variables that are worth further investigation such as: GDP per capita, population density, trade as a share of GDP, economic inequality, mineral rents as a share of GDP and linguistic fractionalization.
The inclusion of categorical variables (income group and politico-geographic regions) in the models shows that these variables are relevant.
The systematic and advanced empirical analysis may be useful for developing data-based recommendations for public policies and legal systems to create settings conductive to a stable democracy.